Super-resolution for imagery from integrated microgrid polarimeters

Abstract

Imagery from microgrid polarimeters is obtained by using a mosaic of pixel-wise micropolarizers on a focal plane array (FPA). Each distinct polarization image is obtained by subsampling the full FPA image. Thus, the effective pixel pitch for each polarization channel is increased and the sampling frequency is decreased. As a result, aliasing artifacts from such undersampling can corrupt the true polarization content of the scene. Here we present the first multi-channel multi-frame super-resolution (SR) algorithms designed specifically for the problem of image restoration in microgrid polarization imagers. These SR algorithms can be used to address aliasing and other degradations, without sacrificing field of view or compromising optical resolution with an anti-aliasing filter. The new SR methods are designed to exploit correlation between the polarimetric channels. One of the new SR algorithms uses a form of regularized least squares and has an iterative solution. The other is based on the faster adaptive Wiener filter SR method. We demonstrate that the new multi-channel SR algorithms are capable of providing significant enhancement of polarimetric imagery and that they outperform their independent channel counterparts.

A collaborative adaptive Wiener filter for multi-frame super-resolution

“…Although the most benefit is seen when the noise level is high. We believe the CAWF SR approach might be beneficial in several other applications as well, including those where the AWF is successful [9][10][11][12][13].…”

“…The AWF SR method uses a weighted sum of registered LR pixels from multiple frames, based on a correlation model and knowledge of the subpixel displacements between frames. In addition to multi-frame SR for grayscale imagery, variations of the AWF SR method have been successfully applied to demosaicing, color SR, and SR processing of polarimetric imagery [9][10][11][12][13]. While the AWF SR method effectively exploits subpixel shifts between temporal frames, it does not exploit self similarity within the observed imagery.…”

Impact of detector-element active-area shape and fill factor on super-resolution

“…For this, we shall follow the approach in Hardie [8], Hardie et al [16], Hardie et al [10] and model diffraction limited optics and blurring from the spatial integration of the detector elements. In this case, the overall OTF is given by where u and v are the horizontal and vertical spatial frequencies in cycles per millimeter, H dif (u, v) is the OTF from the diffraction-limited optics, and detector integration is modeled with H det (u, v).…”

“…An example of a polarimetric division of FPA array is shown in Figure 9 [14,16]. Thus, the active area of a given detector element type must be less than the pitch between like-elements (to make room for the other element types).…”

“…The low fill factor detectors trade signal-to-noise ratio for a more favorable overall system MTF that can be exploited by SR restoration. These results have implications for imaging sensor design for both grayscale and division of FPA sensors (e.g., color, multiband, and polarization) [14][15][16].…”